The proliferation of remote measurement sensors connecting physical objects to the Internet, a technology known as the Internet of Things (IoT), creates new opportunities for manufacturers and service providers by generating large amounts of real-time data about the condition of equipment and other assets. As a result of the availability of data, an increasingly dynamic approach to detailed planning and scheduling of operations can be adopted. This technology is expected to improve the traditionally slow productivity growth in service industries.
In this thesis, our purpose was to examine how the adoption of a dynamic scheduling approach would affect service productivity in a waste collection service, where an investment in a sensor-based scheduling system was under consideration. In this system, sensors would measure the accumulation of waste into containers. To fulfil our purpose, we conducted a discrete-event simulation study, where we compared the performance of the service process using both a dynamic and a static scheduling policy, corresponding to the potential sensor-based and the current practice, respectively. In this comparison, we considered the three dimensions of service productivity: (1) operational costs, (2) service quality, and (3) capacity utilisation. We measured these with three performance measures: travel distance, lateness of collections, and the ratio of capacity utilisation. We conducted the simulation for the currently maintained service level as well as for a range of demand scenarios.
The simulation results show that for the current service level, a dynamic scheduling system would increase travel costs by approximately 20% but improve service quality and capacity utilisation significantly. In order to improve cost-efficiency, the availability of demand data provided by sensors should allow for a decrease in the maintained service level of approximately 25%. However, the decrease in service level required to keep cost savings greater than the investment depends on the costs of implementing the sensor-based scheduling system. We conclude that dynamic scheduling based on remote sensors can be expected to provide efficiency and customer-perceived quality through a more flexible allocation of capacity to meet customer demand, thereby improving total service productivity. However, the scale of the service system is an important factor in making a sensor-based scheduling system a beneficial investment.